A grand challenge in medicine is harnessing control over the behavior of a cell without manipulating it internally. Protein therapeutics have the potential to solve this problem, but identification of such biologics remains difficult because it is not possible to rapidly screen large protein libraries directly on any cell of interest to identify candidates that direct cell phenotype. The goal of this R21 proposal is to develop a broadly useful protein engineering platform that will enable large-scale phenotypic screening of biologics, thus fundamentally transforming the way in which these therapeutics are created. This approach entails creation of a new in vitro display methodology that uses extremely large nave protein libraries (~1011) to select for novel binders to either a specific known target or the entire cell ?surfaceome? (i.e., all cell-surface targets, including recalcitrant proteins that cannot be functionally purified from the plasma membrane but may be important clinical targets). Then, this focused, binder-enriched library (~107) will be the input into another new in vitro display methodology that allows direct screening of individual proteins that alter target cell phenotype as desired. There are several key features of our proposed methodology that contrast it with the current state of the art in the field: 1) the approach can either leverage existing knowledge to target a specific cell-surface receptor or it can be applied without prior bias of targets implicated in the desired cell behavior; 2) the approach is application-agnostic and the target cells do not have to be engineered or grown in the laboratory, so primary cells can be used; 3) the strategy enables recovery of all surfaceome binders, including those to poorly expressed targets or with low binding affinities; 4) in unbiased applications, the recovered biologics can be used to retroactively identify novel cell-surface targets that are implicated in the phenotype transition; and 5) the overall methodology is rapid, so for diseases such as cancer that may evolve and develop resistance in a patient-specific manner, new personalized biologics could be discovered in ?real time? to keep pace with the disease. We anticipate that this new platform ? with its massive increase in throughput and its utility in addressing a broad range of issues in human health and disease ? will be transformative to both translational and fundamental biomedical research.